NMDtxDB: Data-driven identification and annotation of human NMD target transcripts.

The nonsense-mediated RNA decay (NMD) pathway is a crucial mechanism of mRNA quality control. Current annotations of NMD substrate RNAs are rarely data-driven, but use general established rules. We present a dataset with 4 cell lines and combinations for SMG5, SMG6 and SMG7 knockdowns or SMG7 knockout. Based on this dataset, we implemented a workflow that combines Nanopore and Illumina sequencing to assemble a transcriptome, which is enriched for NMD target transcripts. Moreover, we use coding sequence information from Ensembl, Gencode consensus RiboSeq ORFs and OpenProt to enhance the CDS annotation of novel transcript isoforms. In summary, 302,889 transcripts were obtained from the transcriptome assembly process, out of which, 24% are absent from Ensembl database annotations, 48,213 contain a premature stop codon and 6,433 are significantly upregulated in three or more comparisons of NMD active vs deficient cell lines. We present an in-depth view on these results through the NMDtxDB database, which is available at https://shiny.dieterichlab.org/app/NMDtxDB, and supports the study of NMD-sensitive transcripts. We open sourced our implementation of the respective web-application and analysis workflow at https://github.com/dieterich-lab/NMDtxDB and https://github.com/dieterich-lab/nmd-wf.

Transverse aortic constriction multi-omics analysis uncovers pathophysiological cardiac molecular mechanisms.

Time-course multi-omics data of a murine model of progressive heart failure (HF) induced by transverse aortic constriction (TAC) provide insights into the molecular mechanisms that are causatively involved in contractile failure and structural cardiac remodelling. We employ Illumina-based transcriptomics, Nanopore sequencing and mass spectrometry-based proteomics on samples from the left ventricle (LV) and right ventricle (RV, RNA only) of the heart at 1, 7, 21 and 56 days following TAC and Sham surgery. Here, we present Transverse Aortic COnstriction Multi-omics Analysis (TACOMA), as an interactive web application that integrates and visualizes transcriptomics and proteomics data collected in a TAC time-course experiment. TACOMA enables users to visualize the expression profile of known and novel genes and protein products thereof. Importantly, we capture alternative splicing events by assessing differential transcript and exon usage as well. Co-expression-based clustering algorithms and functional enrichment analysis revealed overrepresented annotations of biological processes and molecular functions at the protein and gene levels. To enhance data integration, TACOMA synchronizes transcriptomics and proteomics profiles, enabling cross-omics comparisons. With TACOMA (https://shiny.dieterichlab.org/app/tacoma), we offer a rich web-based resource to uncover molecular events and biological processes implicated in contractile failure and cardiac hypertrophy. For example, we highlight: (i) changes in metabolic genes and proteins in the time course of hypertrophic growth and contractile impairment; (ii) identification of RNA splicing changes in the expression of Tpm2 isoforms between RV and LV; and (iii) novel transcripts and genes likely contributing to the pathogenesis of HF. We plan to extend these data with additional environmental and genetic models of HF to decipher common and distinct molecular changes in heart diseases of different aetiologies. Database URL: https://shiny.dieterichlab.org/app/tacoma.

Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.

The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers. Toward achieving a holistic view of disease, the integration of these transcripts with clinical records and additional data from omic technologies ("multiomic" strategies) has motivated the adoption of artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming a key component of ncRNA-based research. This article presents an overview of the potential and challenges associated with employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers and to decipher ncRNA-associated pathogenetic mechanisms. Methodological and conceptual constraints are discussed, along with an exploration of ethical considerations inherent to AI applications for healthcare and research. The ultimate goal is to provide a comprehensive examination of the multifaceted landscape of this innovative field and its clinical implications.

CircHTT(2,3,4,5,6) - co-evolving with the HTT CAG-repeat tract - modulates Huntington's disease phenotypes.

Circular RNA (circRNA) molecules have critical functions during brain development and in brain-related disorders. Here, we identified and validated a circRNA, circHTT(2,3,4,5,6), stemming from the Huntington's disease (HD) gene locus that is most abundant in the central nervous system (CNS). We uncovered its evolutionary conservation in diverse mammalian species, and a correlation between circHTT(2,3,4,5,6) levels and the length of the CAG-repeat tract in exon-1 of HTT in human and mouse HD model systems. The mouse orthologue, circHtt(2,3,4,5,6), is expressed during embryogenesis, increases during nervous system development, and is aberrantly upregulated in the presence of the expanded CAG tract. While an IRES-like motif was predicted in circH TT (2,3,4,5,6), the circRNA does not appear to be translated in adult mouse brain tissue. Nonetheless, a modest, but consistent fraction of circHtt(2,3,4,5,6) associates with the 40S ribosomal subunit, suggesting a possible role in the regulation of protein translation. Finally, circHtt(2,3,4,5,6) overexpression experiments in HD-relevant STHdh striatal cells revealed its ability to modulate CAG expansion-driven cellular defects in cell-to-substrate adhesion, thus uncovering an unconventional modifier of HD pathology.

APOBEC2 safeguards skeletal muscle cell fate through binding chromatin and regulating transcription of non-muscle genes during myoblast differentiation.

The apolipoprotein B messenger RNA editing enzyme, catalytic polypeptide (APOBEC) family is composed of nucleic acid editors with roles ranging from antibody diversification to RNA editing. APOBEC2, a member of this family with an evolutionarily conserved nucleic acid-binding cytidine deaminase domain, has neither an established substrate nor function. Using a cellular model of muscle differentiation where APOBEC2 is inducibly expressed, we confirmed that APOBEC2 does not have the attributed molecular functions of the APOBEC family, such as RNA editing, DNA demethylation, and DNA mutation. Instead, we found that during muscle differentiation APOBEC2 occupied a specific motif within promoter regions; its removal from those regions resulted in transcriptional changes. Mechanistically, these changes reflect the direct interaction of APOBEC2 with histone deacetylase (HDAC) transcriptional corepressor complexes. We also found that APOBEC2 could bind DNA directly, in a sequence-specific fashion, suggesting that it functions as a recruiter of HDAC to specific genes whose promoters it occupies. These genes are normally suppressed during muscle cell differentiation, and their suppression may contribute to the safeguarding of muscle cell fate. Altogether, our results reveal a unique role for APOBEC2 within the APOBEC family.

OCaR1 endows exocytic vesicles with autoregulatory competence by preventing uncontrolled Ca2+ release, exocytosis, and pancreatic tissue damage.

Regulated exocytosis is initiated by increased Ca2+ concentrations in close spatial proximity to secretory granules, which is effectively prevented when the cell is at rest. Here we showed that exocytosis of zymogen granules in acinar cells was driven by Ca2+ directly released from acidic Ca2+ stores including secretory granules through NAADP-activated two-pore channels (TPCs). We identified OCaR1 (encoded by Tmem63a) as an organellar Ca2+ regulator protein integral to the membrane of secretory granules that controlled Ca2+ release via inhibition of TPC1 and TPC2 currents. Deletion of OCaR1 led to extensive Ca2+ release from NAADP-responsive granules under basal conditions as well as upon stimulation of GPCR receptors. Moreover, OCaR1 deletion exacerbated the disease phenotype in murine models of severe and chronic pancreatitis. Our findings showed OCaR1 as a gatekeeper of Ca2+ release that endows NAADP-sensitive secretory granules with an autoregulatory mechanism preventing uncontrolled exocytosis and pancreatic tissue damage.

Detecting m6A at single-molecular resolution via direct RNA sequencing and realistic training data.

Direct RNA sequencing offers the possibility to simultaneously identify canonical bases and epi-transcriptomic modifications in each single RNA molecule. Thus far, the development of computational methods has been hampered by the lack of biologically realistic training data that carries modification labels at molecular resolution. Here, we report on the synthesis of such samples and the development of a bespoke algorithm, mAFiA (m6A Finding Algorithm), that accurately detects single m6A nucleotides in both synthetic RNAs and natural mRNA on single read level. Our approach uncovers distinct modification patterns in single molecules that would appear identical at the ensemble level. Compared to existing methods, mAFiA also demonstrates improved accuracy in measuring site-level m6A stoichiometry in biological samples.

Targeted Sequencing of Circular RNAs for Illumina-Based Counting and Nanopore Structure Determination.

In the past years, circular RNAs (circRNAs) became a major focus of many studies in animals and plants. circRNAs are generated by backsplicing from the same linear transcripts that are canonically spliced to produce, for example, mature mRNAs. They exhibit tissue-specific expression pattern and are potentially involved in many diseases, among them cardiovascular diseases. However, despite the tremendous efforts to establish circRNA catalogues, much less is known about the biological function of the vast majority of circRNAs. We have previously introduced Lexo-circSeq, a targeted RNA sequencing approach that can profile up to 110 circRNAs and their corresponding linear transcripts in one experiment from low amounts of input material on the Illumina platform. Here, we present an improved protocol for Lexo-circSeq and now extend our approach to Nanopore sequencing, which allows the structural assessment of small- and medium-sized circRNAs. Employing human-induced pluripotent stem-cell-derived cardiomyocytes originating from healthy controls or patients suffering from hypertrophic cardiomyopathy, we identify deregulated circRNAs and alternative exon usage.

Herding Cats in Pandemic Times - Towards Technological and Organizational Convergence of Heterogeneous Solutions for Investigating and Mastering the Pandemic in University Medical Centers.

To understand and handle the COVID-19 pandemic, digital tools and infrastructures were built in very short timeframes, resulting in stand-alone and non-interoperable solutions. To shape an interoperable, sustainable, and extensible ecosystem to advance biomedical research and healthcare during the pandemic and beyond, a short-term project called "Collaborative Data Exchange and Usage" (CODEX+) was initiated to integrate and connect multiple COVID-19 projects into a common organizational and technical framework. In this paper, we present the conceptual design, provide an overview of the results, and discuss the impact of such a project for the trade-off between innovation and sustainable infrastructures.

MePMe-seq: antibody-free simultaneous m6A and m5C mapping in mRNA by metabolic propargyl labeling and sequencing.

Internal modifications of mRNA have emerged as widespread and versatile regulatory mechanism to control gene expression at the post-transcriptional level. Most of these modifications are methyl groups, making S-adenosyl-L-methionine (SAM) a central metabolic hub. Here we show that metabolic labeling with a clickable metabolic precursor of SAM, propargyl-selenohomocysteine (PSH), enables detection and identification of various methylation sites. Propargylated A, C, and G nucleosides form at detectable amounts via intracellular generation of the corresponding SAM analogue. Integration into next generation sequencing enables mapping of N6-methyladenosine (m6A) and 5-methylcytidine (m5C) sites in mRNA with single nucleotide precision (MePMe-seq). Analysis of the termination profiles can be used to distinguish m6A from 2'-O-methyladenosine (Am) and N1-methyladenosine (m1A) sites. MePMe-seq overcomes the problems of antibodies for enrichment and sequence-motifs for evaluation, which was limiting previous methodologies. Metabolic labeling via clickable SAM facilitates the joint evaluation of methylation sites in RNA and potentially DNA and proteins.

CAG repeat expansion in the Huntington's disease gene shapes linear and circular RNAs biogenesis.

Alternative splicing (AS) appears to be altered in Huntington's disease (HD), but its significance for early, pre-symptomatic disease stages has not been inspected. Here, taking advantage of Htt CAG knock-in mouse in vitro and in vivo models, we demonstrate a correlation between Htt CAG repeat length and increased aberrant linear AS, specifically affecting neural progenitors and, in vivo, the striatum prior to overt behavioral phenotypes stages. Remarkably, a significant proportion (36%) of the aberrantly spliced isoforms are not-functional and meant to non-sense mediated decay (NMD). The expanded Htt CAG repeats further reflect on a previously neglected, global impairment of back-splicing, leading to decreased circular RNAs production in neural progenitors. Integrative transcriptomic analyses unveil a network of transcriptionally altered micro-RNAs and RNA-binding proteins (Celf, hnRNPs, Ptbp, Srsf, Upf1, Ythd2) which might influence the AS machinery, primarily in neural cells. We suggest that this unbalanced expression of linear and circular RNAs might alter neural fitness, contributing to HD pathogenesis.

Detection of queuosine and queuosine precursors in tRNAs by direct RNA sequencing.

Queuosine (Q) is a complex tRNA modification found in bacteria and eukaryotes at position 34 of four tRNAs with a GUN anticodon, and it regulates the translational efficiency and fidelity of the respective codons that differ at the Wobble position. In bacteria, the biosynthesis of Q involves two precursors, preQ0 and preQ1, whereas eukaryotes directly obtain Q from bacterial sources. The study of queuosine has been challenging due to the limited availability of high-throughput methods for its detection and analysis. Here, we have employed direct RNA sequencing using nanopore technology to detect the modification of tRNAs with Q and Q precursors. These modifications were detected with high accuracy on synthetic tRNAs as well as on tRNAs extracted from Schizosaccharomyces pombe and Escherichia coli by comparing unmodified to modified tRNAs using the tool JACUSA2. Furthermore, we present an improved protocol for the alignment of raw sequence reads that gives high specificity and recall for tRNAs ex cellulo that, by nature, carry multiple modifications. Altogether, our results show that 7-deazaguanine-derivatives such as queuosine are readily detectable using direct RNA sequencing. This advancement opens up new possibilities for investigating these modifications in native tRNAs, furthering our understanding of their biological function.

Adaptive sampling for nanopore direct RNA-sequencing.

Nanopore long-read sequencing enables real-time monitoring and controlling of individual nanopores. This allows us to enrich or deplete specific sequences in DNA sequencing in a process called "adaptive sampling." So far, adaptive sampling (AS) was not applicable to the direct sequencing of RNA. Here, we show that AS is feasible and useful for direct RNA sequencing (DRS), which has its specific technical and biological challenges. Using a well-controlled in vitro transcript-based model system, we identify essential characteristics and parameter settings for AS in DRS, as the superior performance of depletion over enrichment. Here, the efficiency of depletion is close to the theoretical maximum. Additionally, we demonstrate that AS efficiently depletes specific transcripts in transcriptome-wide sequencing applications. Specifically, we applied our AS approach to poly(A)-enriched RNA samples from human-induced pluripotent stem cell-derived cardiomyocytes and mouse whole heart tissue and show efficient 2.5- to 2.8-fold depletion of highly abundant mitochondrial-encoded transcripts. Finally, we characterize depletion and enrichment performance for complex transcriptome subsets, that is, at the level of the entire Chromosome 11, proving the general applicability of direct RNA AS. Our analyses provide evidence that AS is especially useful to enable the detection of lowly expressed transcripts and reduce the sequencing of highly abundant disturbing transcripts.

Comprehensive identification of diverse ribosomal RNA modifications by targeted nanopore direct RNA sequencing and JACUSA2.

Ribosomal RNAs are decorated by numerous post-transcriptional modifications whose exact roles in ribosome biogenesis, function, and human pathophysiology remain largely unknown. Here, we report a targeted direct rRNA sequencing approach involving a substrate selection step and demonstrate its suitability to identify differential modification sites in combination with the JACUSA2 software. We compared JACUSA2 to other tools designed for RNA modification detection and show that JACUSA2 outperforms other software with regard to detection of base modifications such as methylation, acetylation and aminocarboxypropylation. To illustrate its widespread usability, we applied our method to a collection of CRISPR-Cas9 engineered colon carcinoma cells lacking specific enzymatic activities responsible for particular rRNA modifications and systematically compared them to isogenic wild-type RNAs. Besides the numerous 2'-O methylated riboses and pseudouridylated residues, our approach was suitable to reliably identify differential base methylation and acetylation events. Importantly, our method does not require any prior knowledge of modification sites or the need to train complex models. We further report for the first time detection of human rRNA modifications by direct RNA-sequencing on Flongle flow cells, the smallest-scale nanopore flow cell available to date. The use of these smaller flow cells reduces RNA input requirements, making our workflow suitable for the analysis of samples with limited availability and clinical work.

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision.

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

PEPseq quantifies transcriptome-wide changes in protein occupancy and reveals selective translational repression after translational stress.

Post-transcriptional gene regulation is accomplished by the interplay of the transcriptome with RNA-binding proteins, which occurs in a dynamic manner in response to altered cellular conditions. Recording the combined occupancy of all proteins binding to the transcriptome offers the opportunity to interrogate if a particular treatment leads to any interaction changes, pointing to sites in RNA that undergo post-transcriptional regulation. Here, we establish a method to monitor protein occupancy in a transcriptome-wide fashion by RNA sequencing. To this end, peptide-enhanced pull-down for RNA sequencing (or PEPseq) uses metabolic RNA labelling with 4-thiouridine (4SU) for light-induced protein-RNA crosslinking, and N-hydroxysuccinimide (NHS) chemistry to isolate protein-crosslinked RNA fragments across all long RNA biotypes. We use PEPseq to investigate changes in protein occupancy during the onset of arsenite-induced translational stress in human cells and reveal an increase of protein interactions in the coding region of a distinct set of mRNAs, including mRNAs coding for the majority of cytosolic ribosomal proteins. We use quantitative proteomics to demonstrate that translation of these mRNAs remains repressed during the initial hours of recovery after arsenite stress. Thus, we present PEPseq as a discovery platform for the unbiased investigation of post-transcriptional regulation.

Characterizing alternative splicing effects on protein interaction networks with LINDA.

MOTIVATION: Alternative RNA splicing plays a crucial role in defining protein function. However, despite its relevance, there is a lack of tools that characterize effects of splicing on protein interaction networks in a mechanistic manner (i.e. presence or absence of protein-protein interactions due to RNA splicing). To fill this gap, we present Linear Integer programming for Network reconstruction using transcriptomics and Differential splicing data Analysis (LINDA) as a method that integrates resources of protein-protein and domain-domain interactions, transcription factor targets, and differential splicing/transcript analysis to infer splicing-dependent effects on cellular pathways and regulatory networks. RESULTS: We have applied LINDA to a panel of 54 shRNA depletion experiments in HepG2 and K562 cells from the ENCORE initiative. Through computational benchmarking, we could show that the integration of splicing effects with LINDA can identify pathway mechanisms contributing to known bioprocesses better than other state of the art methods, which do not account for splicing. Additionally, we have experimentally validated some of the predicted splicing effects that the depletion of HNRNPK in K562 cells has on signalling.

Striated muscle-specific base editing enables correction of mutations causing dilated cardiomyopathy.

Dilated cardiomyopathy is the second most common cause for heart failure with no cure except a high-risk heart transplantation. Approximately 30% of patients harbor heritable mutations which are amenable to CRISPR-based gene therapy. However, challenges related to delivery of the editing complex and off-target concerns hamper the broad applicability of CRISPR agents in the heart. We employ a combination of the viral vector AAVMYO with superior targeting specificity of heart muscle tissue and CRISPR base editors to repair patient mutations in the cardiac splice factor Rbm20, which cause aggressive dilated cardiomyopathy. Using optimized conditions, we repair >70% of cardiomyocytes in two Rbm20 knock-in mouse models that we have generated to serve as an in vivo platform of our editing strategy. Treatment of juvenile mice restores the localization defect of RBM20 in 75% of cells and splicing of RBM20 targets including TTN. Three months after injection, cardiac dilation and ejection fraction reach wild-type levels. Single-nuclei RNA sequencing uncovers restoration of the transcriptional profile across all major cardiac cell types and whole-genome sequencing reveals no evidence for aberrant off-target editing. Our study highlights the potential of base editors combined with AAVMYO to achieve gene repair for treatment of hereditary cardiac diseases.

Insights into the FAIRness of the German Network University Medicine: A Survey.

The need to harness large amounts of data, possibly within a short period of time, became apparent during the Covid-19 pandemic outbreak. In 2022, the Corona Data Exchange Platform (CODEX), which had been developed within the German Network University Medicine (NUM), was extended by a number of common components, including a section on FAIR science. The FAIR principles enable research networks to evaluate how well they comply with current standards in open and reproducible science. To be more transparent, but also to guide scientists on how to improve data and software reusability, we disseminated an online survey within the NUM. Here we present the outcomes and lessons learnt.